

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 						_A     (				snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      str					=   MgpstrTokenizer
  __lowerCamelCase			:      Tuple					=   False
  __lowerCamelCase			:      int					=   {}
  __lowerCamelCase			:      Optional[int]					=   False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      super().setUp()

      # fmt: off
      snake_case							:		Optional[int]        =		["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      snake_case							:		Union[str, Any]        =		dict(zip(SCREAMING_SNAKE_CASE_   ,range(len(SCREAMING_SNAKE_CASE_    )    )    )    )

      snake_case							:		Dict        =		os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""vocab_file"""]    )
      with open(self.vocab_file   ,"""w"""   ,encoding="""utf-8"""    ) as fp:
          fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + """\n"""    )





  def    snake_case_       (  self   ,**SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,**SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		str        =		"""tester"""
      snake_case							:		Tuple        =		"""tester"""
      return input_text, output_text





  @unittest.skip("""MGP-STR always lower cases letters."""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_    )
      for tokenizer in tokenizers:
          with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
              snake_case							:		Union[str, Any]        =		"""[SPECIAL_TOKEN]"""

              tokenizer.add_special_tokens({"""cls_token""": special_token}    )
              snake_case							:		str        =		tokenizer.encode([special_token]   ,add_special_tokens=SCREAMING_SNAKE_CASE_    )
              self.assertEqual(len(SCREAMING_SNAKE_CASE_    )   ,1    )

              snake_case							:		Dict        =		tokenizer.decode(SCREAMING_SNAKE_CASE_   ,skip_special_tokens=SCREAMING_SNAKE_CASE_    )
              self.assertTrue(special_token not in decoded    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		self.get_tokenizers()
      for tokenizer in tokenizers:
          with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
              snake_case      ,		snake_case							:		List[str]        =		self.get_input_output_texts(SCREAMING_SNAKE_CASE_    )

              snake_case							:		Any        =		tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
              snake_case							:		str        =		tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )
              snake_case							:		Optional[Any]        =		tokenizer.encode(SCREAMING_SNAKE_CASE_   ,add_special_tokens=SCREAMING_SNAKE_CASE_    )
              self.assertListEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

              snake_case							:		Dict        =		tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    )
              self.assertNotEqual(len(SCREAMING_SNAKE_CASE_    )   ,0    )
              snake_case							:		Any        =		tokenizer.decode(SCREAMING_SNAKE_CASE_    )
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

              self.assertEqual(text_a.replace(""" """   ,""""""    )   ,SCREAMING_SNAKE_CASE_    )





  @unittest.skip("""MGP-STR tokenizer only handles one sequence."""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


